Sound Perception Test

Understand how your sounds are perceived.

A structured listening study with independent listeners, designed to compare sound variants and show where the evidence is clear — and where it isn't.

Why this exists

Everyone on the team hears something different.

One person says variant A feels calmer. Another says B is more urgent, in a good way. Nobody's wrong — perception is exactly that personal. But a product decision needs more than confident opinions in a meeting room. It needs evidence from people who don't know your product, your roadmap, or which version you prefer.

How it works

A structured process from question to evidence.

DoReMiFo runs this pipeline for its own ongoing perceptual research — a live testing environment, an active participant pool, and an evaluation methodology already in use.

1

You send your sounds and the decision

What you're deciding, and for whom.

2

DoReMiFo designs the experiment

Using an evaluation methodology already in use in ongoing research.

3

Independent listeners are recruited

Through an existing, active Prolific account, screened for attention and consistency.

4

The listening test runs

On DoReMiFo's own testing environment, already proven at scale, able to test dozens of sounds per round.

5

The data is analyzed

AI-assisted statistical modeling — see "Where AI is — and isn't" below.

6

You receive the evidence

How each sound lands, the variant comparison, Decision Confidence — and one of three calls: Ship, Revise, or Evidence inconclusive.

What you get

A clear choice when the evidence supports one.
A clear limit when it doesn't.

  • How each sound lands — tone, energy and the meaning listeners read into it
  • Direct comparison between variants
  • Intended meaning: clearly recognised or ambiguous — how each sound reads against the event it has to signal
  • Decision Confidence — the strength of evidence behind the call
  • One of three calls: Ship, Revise, or Evidence inconclusive
Where AI fits

AI-assisted, not AI-decided.

Analysis is AI-assisted: statistical models turn listener responses into the profile, comparison and confidence estimates in your report. The listening itself isn't — every rating comes from a real, independently recruited listener, not a prediction.

2,970listener ratings tested on, in DoReMiFo's ongoing research
0.74–0.91reliability (ICC) of a sound's average rating across different listener groups

ICC measures how stable a sound's average rating stays when you draw a different group of listeners — not how much two individual people agree with each other. It's a property of the panel-level measurement, not a claim that any two listeners rate a sound identically.

A person on the DoReMiFo team reviews the analysis and interpretation before it reaches you. AI assists the process, it doesn't sign off on your result.

Research lab

Built on the same methodology as the research.

DoReMiFo is a research lab: commercial listening studies (DoReMiFo s.r.o.) and academic research (HAMU Prague, SGS grant) inform each other while remaining separately funded and governed.

client sounds → listening experiments → measured evidence → model research → better tools for sound design

Using client data for further research is never automatic — it depends on the agreement in place for that engagement.

Rigorous about what we know.
Explicit about what we don't.

If the evidence does not support a meaningful difference, we say so.

Institution

HAMU Prague

Funding

SGS Academic Grant

Emotional model

Russell Circumplex (1980)

Feature extraction

Essentia · active-segment

Real example

Two "error" sounds. Same job. Very different profile.

From DoReMiFo's own calibration measurements: two everyday error sounds you already know, rated by independent listeners on emotional tone (positive–negative) and energy (calm–high-energy). Anonymized here — the point is the gap between them, not the brand.

HIGH-ENERGY CALM NEGATIVE POSITIVE 2 Error sound 2 1 Error sound 1
Error sound 1Mildly negative in tone, moderately high-energy (n = 25 listeners)
Error sound 2Clearly more negative in tone, calmer (n = 27 listeners)

Two sounds doing the exact same job — telling you something went wrong — can land in very different places. That gap is exactly what a Sound Perception Test is built to catch before you ship, not guess at after.

Where it applies

Built for the sounds that shape everyday interaction.

Product and UI sounds

The small sounds that confirm, deny, or guide an action.

Notifications, confirmations and alerts

Moments that need the right urgency — not just more of it.

Game sound effects

Feedback that shapes how an action feels, round after round.

HMI and device sounds

Interface sound for physical products, appliances and vehicles.

Short sonic branding assets

A few seconds of sound that carry an identity.

FAQ

Questions worth answering upfront.

How long does a test take?

A few days from your brief — send your sounds and the decision you're making, and we'll confirm the exact timeline before you commit.

How many listeners are involved?

Our target is 25 independent listener ratings per sound — enough for a statistically meaningful comparison, consistent with DoReMiFo's established methodology.

Is my data used for future research?

Not automatically — it depends on the agreement in place for your engagement.

What if listeners don't agree with each other?

That's exactly what the test is designed to surface. Your report includes the strength and consistency of the evidence, not just an average — if listeners are split, we say so instead of picking a winner for you.

Do you compose or generate sounds?

Not within the Sound Perception Test — this service measures and evaluates sounds you already have. It doesn't compose or generate audio for you.

Considering a listening study?

Not sure whether a listening test fits your decision?

You do not need to prepare a brief or upload sounds. A short description of what your team is comparing is enough.

Email Bohdan